Allocating operators of a streaming application to virtual machines based on monitored performance

ABSTRACT

Performance thresholds are defined for operators in a flow graph for a streaming application. A streams manager deploys the flow graph to one or more virtual machines (VMs). The performance of each portion of the flow graph on each VM is monitored. A VM is selected. When the performance of the portion of the flow graph in the selected VM does not satisfy the defined performance threshold(s), a determination is made regarding whether the portion of the flow graph is underperforming or overperforming. When the portion of the flow graph is underperforming, the portion of the flow graph is split into multiple portions that are implemented on multiple VMs. When the portion of the flow graph is overperforming, a determination is made of whether a neighbor VM is also overperforming. When a neighbor VM is also overperforming, the two VMs may be coalesced into a single VM.

BACKGROUND

1. Technical Field

This disclosure generally relates to streaming applications, and morespecifically relates to monitoring performance of a streamingapplication and allocating operators to virtual machines based on themonitored performance.

2. Background Art

Streaming applications are known in the art, and typically includemultiple operators coupled together in a flow graph that processstreaming data in near real-time. An operator typically takes instreaming data in the form of data tuples, operates on the tuples insome fashion, and outputs the processed tuples to the next operator.Streaming applications are becoming more common due to the highperformance that can be achieved from near real-time processing ofstreaming data.

Many streaming applications require significant computer resources, suchas processors and memory, to provide the desired near real-timeprocessing of data. However, the workload of a streaming application canvary greatly over time. Allocating on a permanent basis computerresources to a streaming application that would assure the streamingapplication would always function as desired (i.e., during peak demand)would mean many of those resources would sit idle when the streamingapplication is processing a workload significantly less than itsmaximum. Furthermore, what constitutes peak demand at one point in timecan be exceeded as the usage of the streaming application increases. Fora dedicated system that runs a streaming application, an increase indemand may require a corresponding increase in hardware resources tomeet that demand.

BRIEF SUMMARY

Performance thresholds are defined for operators in a flow graph for astreaming application. A streams manager deploys the flow graph to oneor more virtual machines (VMs). The performance of each portion of theflow graph on each VM is monitored. A VM is selected. When theperformance of the portion of the flow graph deployed on the selected VMdoes not satisfy the defined performance threshold(s), a determinationis made regarding whether the portion of the flow graph isunderperforming or overperforming. When the portion of the flow graph inthe selected VM is underperforming, the portion of the flow graph issplit into multiple portions that are implemented on multiple VMs. Whenthe portion of the flow graph in the selected VM is overperforming, adetermination is made of whether a neighbor VM is also overperforming.When a neighbor VM is also overperforming, the two VMs may be coalescedinto a single VM. In this manner performance of a streaming applicationmay be dynamically adjusted as needed.

The foregoing and other features and advantages will be apparent fromthe following more particular description, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The disclosure will be described in conjunction with the appendeddrawings, where like designations denote like elements, and:

FIG. 1 is a block diagram of a cloud computing node;

FIG. 2 is a block diagram of a cloud computing environment;

FIG. 3 is a block diagram of abstraction model layers;

FIG. 4 is a block diagram showing some features of a cloud manager;

FIG. 5 is a block diagram showing some features of a streams manager;

FIG. 6 is a flow diagram of a method for defining one or moreperformance thresholds for operators in a flow graph of a streamingapplication;

FIG. 7 is a flow diagram of a method for a streams manager to interactwith a cloud manager to deploy a portion of a flow graph to one or moreVMs;

FIG. 8 is a flow diagram of a method for a streams manager to monitorperformance of each portion of a flow graph deployed on different VMs,and to dynamically adjust the allocation of VMs to the streamingapplication;

FIG. 9 is a block diagram showing an example of a flow graph of asuitable streaming application;

FIG. 10 is a block diagram showing allocation of the flow graph in FIG.9 to two VMs after splitting the flow graph into two portions;

FIG. 11 is a block diagram showing allocation of the flow graph in FIG.10 to three VMs after splitting the portion of the flow graph thatresided in VM2 in FIG. 10 into two portions that are deployed toseparate VMs;

FIG. 12 is a block diagram showing allocation of the flow graph in FIG.9 to four VMs after splitting the portion of the flow graph that residedin VM2 in FIG. 11 into two portions that are deployed to separate VMs;and

FIG. 13 is a block diagram showing allocation of the flow graph in FIG.12 to three VMs by coalescing the portions of the flow graph that werein VM3 and VM4 in FIG. 12 to a single VM.

DETAILED DESCRIPTION

The disclosure and claims herein relate to a streams manager thatdeploys a flow graph for a streaming application to one or more virtualmachines (VMs). Performance thresholds are defined for operators in theflow graph. The performance of each portion of the flow graph on each VMis monitored. A VM is selected. When the performance of the portion ofthe flow graph deployed on the selected VM does not satisfy the definedperformance threshold(s), a determination is made regarding whether theportion of the flow graph is underperforming or overperforming. When theportion of the flow graph in the selected VM is underperforming, theportion of the flow graph is split into multiple portions that areimplemented on multiple VMs. When the portion of the flow graph in theselected VM is overperforming, a determination is made of whether aneighbor VM is also overperforming. When a neighbor VM is alsooverperforming, the two VMs are coalesced into a single VM. In thismanner performance of a streaming application may be dynamicallyadjusted as needed.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, tabletcomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processing unit 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media. An example ofremovable media is shown in FIG. 1 to include a Digital Video Disc (DVD)192.

System memory 130 can include computer system readable media in the formof volatile or non-volatile memory, such as firmware 132. Firmware 132provides an interface to the hardware of computer system/server 110.System memory 130 can also include computer system readable media in theform of volatile memory, such as random access memory (RAM) 134 and/orcache memory 136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. Such communicationcan occur via Input/Output (I/O) interfaces 170. Still yet, computersystem/server 110 can communicate with one or more networks such as alocal area network (LAN), a general wide area network (WAN), and/or apublic network (e.g., the Internet) via network adapter 160. Asdepicted, network adapter 160 communicates with the other components ofcomputer system/server 110 via bus 122. It should be understood thatalthough not shown, other hardware and/or software components could beused in conjunction with computer system/server 110. Examples, include,but are not limited to: microcode, device drivers, redundant processingunits, external disk drive arrays, Redundant Array of Independent Disk(RAID) systems, tape drives, data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 in FIG. 2 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM System z systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM System p systems; IBMSystem x systems; IBM BladeCenter systems; storage devices; networks andnetworking components. Examples of software components include networkapplication server software, in one example IBM WebSphere® applicationserver software; and database software, in one example IBM DB2® databasesoftware. IBM, System z, System p, System x, BladeCenter, WebSphere, andDB2 are trademarks of International Business Machines Corporationregistered in many jurisdictions worldwide.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. A cloud manager 350 is representative of a cloudmanager as described in more detail below. While the cloud manager 350is shown in FIG. 3 to reside in the management layer 330, cloud manager350 can span all of the levels shown in FIG. 3, as discussed below.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and a streams manager 360, as discussed in more detailbelow.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 4 shows one suitable example of the cloud manager 350 shown in FIG.3. The cloud manager 350 includes a cloud provisioning mechanism 410that includes a resource request interface 420. The resource requestinterface 420 allows a software entity, such as the streams manager 360,to request virtual machines from the cloud manager 350 without humanintervention. The cloud manager 350 also includes a user interface 430that allows a user to interact with the cloud manager to perform anysuitable function, including provisioning of VMs, destruction of VMs,performance analysis of the cloud, etc. The difference between theresource request interface 420 and the user interface 430 is a user mustmanually use the user interface 430 to perform functions specified bythe user, while the resource request interface 420 may be used by asoftware entity to request provisioning of cloud resources by the cloudmechanism 350 without input from a human user. Of course, cloud manager350 could include many other features and functions known in the artthat are not shown in FIG. 4.

FIG. 5 shows one suitable example of the streams manager 360 shown inFIG. 3. The streams manager 360 is software that manages one or morestreaming applications, including creating operators and data flowconnections between operators in a flow graph that represents astreaming application. The streams manager 360 includes a performancemonitor 510 with one or more performance thresholds 520. Performancethresholds 520 can include static thresholds, such as tuple rates, andcan also include any suitable heuristic for measuring performance of astreaming application as a whole or for measuring performance of one ormore operators in a streaming application. Performance thresholds 520may include different thresholds and metrics at the operator level, atthe level of a group of operators that make up a portion of the flowgraph, and at the level of the overall performance of the streamingapplication. The stream performance monitor 510 monitors performance ofa streaming application, and when current performance compared to theone or more performance thresholds 520 indicates a portion of thestreaming application is underperforming or overperforming, the streamperformance monitor 510 can take appropriate action, such as splittingan underperforming portion of the flow graph running on a VM into twoportions that run on different VMs, or coalescing two portions of theflow graph that are both overperforming and running on two neighboringVMs into a single VM. Note the streams manager 360 communicates the needfor resources to the cloud resource request mechanism 530. The cloudresource request mechanism 530, in response to the communication fromthe stream performance monitor, assembles a cloud resource request 540,which can include information such as a number of VMs to provision 550,stream infrastructure needed in each VM 560, and a stream applicationportion 570 for each VM. Once the cloud resource request 530 isformulated, the streams manager 360 submits the cloud resource request540 to a cloud manager, such as cloud manager 350 shown in FIGS. 3 and4.

The cloud resource request can be formatted in any suitable way. Asimple example will illustrate two suitable ways for formatting a cloudresource request. Let's assume the streams manager determines it needstwo VMs, where both have common stream infrastructure, with a first ofthe VMs hosting operator A and the second of the VMs hosting operator B.The cloud resource request 540 in FIG. 5 could specify two VMs at 550,could specify the common stream infrastructure, such as an operatingsystem and middleware, at 560, and could specify operator A and operatorB at 570. In response, the cloud manager would provision two VMs withthe common stream infrastructure, with the first of the VMs hostingoperator A and the second of the VMs hosting operator B. In thealternative, the cloud resource request 540 could be formulated suchthat each VM is specified with its corresponding stream infrastructureand stream application portion. In this configuration, the cloudresource request would specify a first VM with the common streaminfrastructure and operator A, and a second VM with the common streaminfrastructure and operator B.

Referring to FIG. 6, a method 600 defines one or more performancethresholds for operators in the flow graph (step 610). The performancethresholds defined in step 610 are included in the performancethresholds 520 shown in FIG. 5. As explained above, the performancethresholds can include any suitable static threshold or heuristic fordetermining when a single operator is underperforming andoverperforming, for determining when any group of operators in the flowgraph is underperforming and overperforming, and for determining whenthe streaming application as a whole is underperforming oroverperforming.

A simple example will illustrate. In one specific implementation, theperformance thresholds include a lower threshold of tuple rate for eachoperator that defines the desired minimum performance of the operatorand an upper threshold of tuple rate for each operator that defines thedesired maximum performance of the operator. When the operator has athroughput that is less than the lower threshold, the operator isunderperforming. When the operator has a throughput that is more thanthe upper threshold, the operator is overperforming. Similar principlescould be applied to groups of operators, and to the streamingapplication as a whole. As used herein, the term “underperforming” meanssome portion of the flow graph (e.g., a single operator, multipleoperators, or the entire flow graph) has performance that needs to beincreased, while “overperforming” means the portion of the flow graphhas more performance than it needs.

FIG. 7 shows a method 700 for the streams manager to deploy portions ofa streaming application to one or more VMs. The streams manager requestsresources, such as VMs, from the cloud manager (step 710). The cloudmanager provisions the VMs (step 720). The streams manager then deploysa portion of the flow graph to the VMs (step 730). In the most preferredimplementation, when the streams manager deploys a portion of a flowgraph to a VM, it does so using method 700.

FIG. 8 shows one suitable example of a method 800 for running astreaming application. The streams manager deploys the flow graph to oneor more VMs (step 805). Step 805 is one suitable implementation for step730 in FIG. 7. In one suitable implementation, the flow graph isinitially deployed to a single VM. However, in an alternativeimplementation, the flow graph can be deployed to multiple VMs. The flowgraph could be divided into different portions in any suitable way andusing any suitable heuristic for initially deploying the flow graph tomultiple VMs. The performance of the portion of the flow graph in eachVM is monitored (step 810). When the performance satisfies theperformance thresholds (step 820=YES), no corrective action is needed,so method 800 returns to step 810. When the performance of one or moreportions of the streaming application does not satisfy one or more ofthe performance thresholds (step 820=NO), a VM that includes a portionthat does not satisfy the performance threshold(s) is selected (step830). A determination is then made regarding whether the portion of theflow graph that does not satisfy the performance threshold(s) isunderperforming or overperforming (step 840). When the portion of theflow graph is underperforming (step 840=Underperforming), theunderperforming portion of the flow graph is split to run on multipleVMs (step 850). This split can be made in any suitable way using anysuitable heuristic. For example, the split can be made horizontally orvertically using any suitable criteria for performing the split. Theheuristic could a single heuristic, or there could be multipleheuristics, one of which could be selected according to the results ofthe performance monitoring in step 810.

When the portion of the flow graph in the selected VM is overperforming(step 840=Overperforming), method 800 determines whether a neighbor VMis also overperforming (step 860). As used herein, a neighbor VM is a VMthat includes an operator that communicates with an operator in theselected VM. When the selected VM has a neighbor VM that is alsooverperforming (step 860=YES), the portion of the flow graph in theselected VM and the portion of the flow graph in the neighbor VM can becoalesced into a single VM (step 870). Note the single VM in step 870could be the selected VM, the neighbor VM, or a different VM.

Note that method 800 loops to continuously monitor performance of allportions of the flow graph, and dynamically changes allocation ofportions of the flow graph that are underperforming by splitting atleast one portion of the flow graph to different VMs (step 850) andchanges allocation of portions of the flow graph that are overperformingby coalescing at least one portion of the flow graph to a single VM(step 870).

Examples are now presented to illustrate the concepts discussed abovewith respect to FIGS. 6-8. One example of a flow graph is shown in FIG.9 that includes operators labeled A through M. We assume for thisexample the flow graph in FIG. 9 is initially deployed to a single VMsuch that the entire streaming application in FIG. 9 resides on a singleVM. We assume the streaming application in FIG. 9 is underperforming,which means the streaming application in FIG. 9 does not satisfy one ormore of the performance thresholds (step 820=NO). The single VM thatcontains the entire streaming application shown in FIG. 9 is selected(step 830). Because the streaming application is underperforming (step840=Underperforming), the streaming application is split into multipleVMs, as shown in FIG. 10. As discussed above, the split can be madeusing any suitable criteria or heuristic. We assume the split in FIG. 10results in the portion of the flow diagram in VM1 satisfying theperformance thresholds, but the portion of the flow diagram in VM2 isstill underperforming. The portion in VM2 is thus split again betweenVM2 and VM3, as shown in FIG. 11. We assume the split in FIG. 11 resultsin the portions of the flow diagram in VM1 and VM3 satisfying theperformance thresholds, but the portion of the flow diagram in VM2 isstill underperforming. The portion in VM2 is thus split again betweenVM2 and VM4, as shown in FIG. 12. We assume at this point that allportions in all four VMs in FIG. 12 satisfy the performance thresholds.

Let's assume the splitting done in FIGS. 10, 11 and 12 was necessary dueto a heavy load on the streaming application. Let's further assume theheavy load is reduced to a more reasonable load, which results in VM3and VM4 both overperforming (step 840=Overperforming). Because these areneighbor nodes by having operators connected to each other, and becauseboth are overperforming, the portions of the flow graph implemented inVM3 and VM4 can be coalesced into a single VM, as shown at VM3 in FIG.13. This simple example shows how a streams manager can dynamicallysplit portions of a flow graph and can dynamically coalesce portions ofa flow graph as needed to satisfy the defined performance thresholds.Because the splitting and coalescing is done on a dynamic and ongoingbasis, the deployment of the flow graph to VMs can change as needed toaddress performance issues.

The figures and description above support an apparatus comprising: atleast one processor; a memory coupled to the at least one processor; astreaming application residing in the memory and executed by the atleast one processor, the streaming application comprising a flow graphthat includes a plurality of operators that process a plurality of datatuples; a plurality of performance thresholds residing in the memory forthe plurality of operators in the flow graph; and a streams managerresiding in the memory and executed by the at least one processor, thestreams manager deploying the flow graph on at least one virtual machine(VM), monitoring performance of the streaming application, and whenperformance of a first portion of the streaming application in aselected VM does not satisfy at least one of the plurality ofperformance thresholds, the streams manager determines when the firstportion of the streaming application in the selected VM isunderperforming, and when the first portion of the streaming applicationin the selected VM is underperforming, the first portion of thestreaming application in the selected VM is split into second and thirdportions of the streaming application that are deployed to a pluralityof VMs.

The figures and description above additionally support acomputer-implemented method executed by at least one processor forrunning a streaming application, the method comprising: deploying astreaming application that comprises a flow graph that includes aplurality of operators that process a plurality of data tuples on atleast one virtual machine (VM); defining a plurality of performancethresholds for the plurality of operators in the flow graph; monitoringperformance of the streaming application; when performance of a firstportion of the streaming application in a selected VM does not satisfyat least one of the plurality of performance thresholds, determiningwhen the first portion of the streaming application in the selected VMis underperforming, and when the first portion of the streamingapplication in the selected VM is underperforming, splitting the firstportion of the streaming application in the selected VM into second andthird portions of the streaming application that are deployed to aplurality of VMs.

The figures and description above further support a computer-implementedmethod executed by at least one processor for running a streamingapplication, the method comprising: deploying a streaming applicationthat comprises a flow graph that includes a plurality of operators thatprocess a plurality of data tuples on a single virtual machine (VM) byrequesting a cloud manager provision the single VM, and after the cloudmanager provisions the single VM, the streams manager deploys the flowgraph to the single VM; defining a plurality of performance thresholdsfor the plurality of operators in the flow graph, wherein the pluralityof performance thresholds includes at least one threshold that indicateswhen at least one operator in the flow graph is underperforming and atleast one threshold that indicates when at least one operator in theflow graph is overperforming; monitoring performance of the streamingapplication; when performance of the streaming application in the singleVM does not satisfy at least one of the plurality of performancethresholds, determining when the streaming application in the single VMis underperforming, and when the streaming application in the single VMis underperforming, splitting the streaming application in the single VMinto first and second portions of the streaming application that aredeployed to first and second VMs by requesting the cloud managerprovision the first and second VMs; when performance of the firstportion of the streaming application in the first VM does not satisfy atleast one of the plurality of performance thresholds, determining whenthe first portion of the streaming application in the first VM isunderperforming, and when the first portion of the streaming applicationin the first VM is underperforming, splitting the first portion of thestreaming application in the first VM into third and fourth portions ofthe streaming application that are deployed to respective third andfourth VMs by requesting the cloud manager provision the third andfourth VMs; when performance of the second portion of the streamingapplication in the second VM does not satisfy at least one of theplurality of performance thresholds, determining when the second portionof the streaming application in the second VM is underperforming, andwhen the second portion of the streaming application in the second VM isunderperforming, splitting the second portion of the streamingapplication in the second VM into fifth and sixth portions of thestreaming application that are deployed to respective fifth and sixthVMs; when performance of the first portion of the streaming applicationin the first VM does not satisfy the at least one of the plurality ofperformance thresholds, determining when the first portion of thestreaming application in the first VM is overperforming, and when thefirst portion of the streaming application in the first VM isoverperforming and when a different VM that has an operator in a seventhportion of the flow graph that communicates with an operator in thefirst portion of the streaming application in the selected VM is alsooverperforming, coalescing the first portion and the seventh portion torun on a single VM; and continuously monitoring performance of allportions of the flow graph and dynamically changing allocation ofportions of the flow graph that are underperforming by splitting atleast one portion of the flow graph to different VMs and changingallocation of portions of the flow graph that are overperforming bycoalescing at least one portion of the flow graph to a single VM.

The disclosure and claims herein relate to a streams manager thatdeploys a flow graph for a streaming application to one or more virtualmachines (VMs). Performance thresholds are defined for operators in theflow graph. The performance of each portion of the flow graph on each VMis monitored. A VM is selected. When the performance of the portion ofthe flow graph deployed on the selected VM does not satisfy the definedperformance threshold(s), a determination is made regarding whether theportion of the flow graph is underperforming or overperforming. When theportion of the flow graph in the selected VM is underperforming, theportion of the flow graph is split into multiple portions that areimplemented on multiple VMs. When the portion of the flow graph in theselected VM is overperforming, a determination is made of whether aneighbor VM is also overperforming. When a neighbor VM is alsooverperforming, the two VMs are coalesced into a single VM. In thismanner performance of a streaming application may be dynamicallyadjusted as needed.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

1-7. (canceled)
 8. A computer-implemented method executed by at leastone processor for running a streaming application, the methodcomprising: deploying a streaming application that comprises a flowgraph that includes a plurality of operators that process a plurality ofdata tuples on at least one virtual machine (VM); defining a pluralityof performance thresholds for the plurality of operators in the flowgraph; monitoring performance of the streaming application; and whenperformance of a first portion of the streaming application in aselected VM does not satisfy at least one of the plurality ofperformance thresholds, determining when the first portion of thestreaming application in the selected VM is overperforming, and when thefirst portion of the streaming application in the selected VM isoverperforming and when a second VM that has an operator in a secondportion of the flow graph that communicates with an operator in thefirst portion of the streaming application in the selected VM is alsooverperforming, coalescing the first portion and the second portion torun on a single VM.
 9. The method of claim 8 wherein when performance ofthe first portion of the streaming application in the selected VM doesnot satisfy the at least one of the plurality of performance thresholds,determining when the first portion of the streaming application in theselected VM is underperforming, and when the first portion of thestreaming application in the selected VM is underperforming, splittingthe first portion of the streaming application in the selected VM intothird and fourth portions of the streaming application that are deployedto a plurality of VMs.
 10. The method of claim 8 wherein deploying thestreaming application on the at least one VM comprises initiallydeploying the flow graph on a single VM.
 11. The method of claim 8wherein deploying the streaming application on the at least one VMcomprises requesting a cloud manager provision the plurality of VMs, andafter the cloud manager provisions the plurality of VMs, the streamsmanager deploys the flow graph to the plurality of VMs.
 12. The methodof claim 8 wherein the plurality of performance thresholds includes atleast one threshold that indicates when at least one operator in theflow graph is underperforming.
 13. The method of claim 8 wherein theplurality of performance thresholds includes at least one threshold thatindicates when at least one operator in the flow graph isoverperforming.
 14. The method of claim 8 further comprisingcontinuously monitoring performance of all portions of the flow graphand dynamically changing allocation of portions of the flow graph thatare underperforming by splitting at least one portion of the flow graphto different VMs and changing allocation of portions of the flow graphthat are overperforming by coalescing at least one portion of the flowgraph to a single VM.
 15. A computer-implemented method executed by atleast one processor for running a streaming application, the methodcomprising: deploying a streaming application that comprises a flowgraph that includes a plurality of operators that process a plurality ofdata tuples on a single virtual machine (VM) by requesting a cloudmanager provision the single VM, and after the cloud manager provisionsthe single VM, the streams manager deploys the flow graph to the singleVM; defining a plurality of performance thresholds for the plurality ofoperators in the flow graph, wherein the plurality of performancethresholds includes at least one threshold that indicates when at leastone operator in the flow graph is underperforming and at least onethreshold that indicates when at least one operator in the flow graph isoverperforming; monitoring performance of the streaming application;when performance of the streaming application in the single VM does notsatisfy at least one of the plurality of performance thresholds,determining when the streaming application in the single VM isunderperforming, and when the streaming application in the single VM isunderperforming, splitting the streaming application in the single VMinto first and second portions of the streaming application that aredeployed to first and second VMs by requesting the cloud managerprovision the first and second VMs; when performance of the firstportion of the streaming application in the first VM does not satisfy atleast one of the plurality of performance thresholds, determining whenthe first portion of the streaming application in the first VM isunderperforming, and when the first portion of the streaming applicationin the first VM is underperforming, splitting the first portion of thestreaming application in the first VM into third and fourth portions ofthe streaming application that are deployed to respective third andfourth VMs by requesting the cloud manager provision the third andfourth VMs; when performance of the second portion of the streamingapplication in the second VM does not satisfy at least one of theplurality of performance thresholds, determining when the second portionof the streaming application in the second VM is underperforming, andwhen the second portion of the streaming application in the second VM isunderperforming, splitting the second portion of the streamingapplication in the second VM into fifth and sixth portions of thestreaming application that are deployed to respective fifth and sixthVMs; when performance of the first portion of the streaming applicationin the first VM does not satisfy the at least one of the plurality ofperformance thresholds, determining when the first portion of thestreaming application in the first VM is overperforming, and when thefirst portion of the streaming application in the first VM isoverperforming and when a different VM that has an operator in a seventhportion of the flow graph that communicates with an operator in thefirst portion of the streaming application in the selected VM is alsooverperforming, coalescing the first portion and the seventh portion torun on a single VM; and continuously monitoring performance of allportions of the flow graph and dynamically changing allocation ofportions of the flow graph that are underperforming by splitting atleast one portion of the flow graph to different VMs and changingallocation of portions of the flow graph that are overperforming bycoalescing at least one portion of the flow graph to a single VM.